OFICIAL Mistral AI News

Modernizing complex legacy code with AI agents.

What happened
Based on Mistral AI News · Sep 09, 2026

Mistral AI assisted a European energy operator in migrating 40,000 lines of Fortran 77 legacy code to C++, addressing structural and documentation challenges through AI-driven workflows and human oversight.

Modernizing complex legacy code with AI agents.
Mistral AI News — Mistral AI
Key points
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Mistral AI migrated 40,000 lines of Fortran 77 to C++ for a European energy operator using AI agents and human oversight.
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Agents instrumented Fortran code to dump state snapshots like RHOG (42.71834) for numerical verification in C++ modules.
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A hybrid workflow with human checkpoints and agent roles improved code quality and unblocked stalled migrations.

Mistral AI supported a European energy operator in modernizing a 40,000-line Fortran 77 physics simulator by migrating it to C++. The legacy code lacked test suites and centralized documentation, reflecting Fortran 77’s constraints such as implicit typing, COMMON blocks for global state, and cryptic variable names limited to six characters. Translating syntax alone proved insufficient, as architectural differences between procedural Fortran and object-oriented C++ required refactoring to avoid merely replicating Fortran logic in C++ syntax.

The migration demanded numerical parity between the original and migrated code, verified through instrumented state snapshots and test frameworks. Agents inserted checks to dump critical variables like RHOG (42.71834) in Fortran, using these values as reference points for testing migrated C++ modules. Establishing this verification harness early ensured long agent runs remained safe and provided clear evidence of successful migration, a step Mistral recommends for similar modernization projects.

Documentation scattered across PDFs and embedded comments was consolidated by generating a caller-callee tree from the Fortran codebase using a custom parser. Over a hundred agents, equipped with document libraries and Mistral OCR, documented the tree from leaves upward, opening pull requests for each node. A reviewer agent monitored these PRs, requesting fixes as needed, though initial attempts at full autonomy produced code that resembled Fortran syntax rather than modernized C++.

The project ultimately adopted a structured workflow combining human oversight with agent roles: a planner, coder, tester, and reviewer for each module. Human intervention was introduced to unblock agents when they encountered bugs, balancing code quality with progress. Modules under 10,000 lines were identified as independent units, with the first sprint covering 40,000 of 300,000 total lines, demonstrating a scalable approach to legacy code modernization.

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